Image registration aims to spatially align one image to another. For that purpose, parameters of a global transformation model, such as a rigid, affine or projective transformation, are to be recovered to geometrically transform a moving image to achieve high spatial correspondence with a fixed image. The problem has been studied in various contexts due to its significance in a wide range of areas, including medical image fusion, remote sensing, recognition, tracking, mosaicing, and so on.
Rigid registration of 2D/3D medical images is a vital component of a large number of registration and fusion applications. In the areas of diagnosis, planning, evaluation of surgical and radio-therapeutical procedures, typically multiple single-modality, or multi-modality images are acquired in the clinical track of events. Since these images are complementary to each other, the integration of useful data from separate images are often desired. Rigid registration, a first step in this integration process, aims to bring the multiple images involved into spatial alignment.
Existing methods for image registration can be classified into three categories: feature-based methods, intensity-based methods, and hybrid methods that integrate the previous two. Feature-based methods use sparse geometric features such as points, curves, and/or surface patches, and their correspondences to compute an optimal transformation. These methods are relatively fast. However, these methods lack robustness of feature extraction and accuracy of feature correspondences, and have frequent need of user interaction. Intensity-based registration methods operate directly on the intensity values from the full content of the image, without prior feature extraction. These methods have attracted attention because they can be made fully automatic and can be used for multimodality image matching by utilizing appropriate similarity measures. However, these methods tend to have high computational cost due to the need for optimization of complex, non-convex energy functions. In addition, they require the poses of two input images be close enough to converge to a local optimum. Furthermore, they often perform poorly when partial matching is required.
Recently, several hybrid methods have been proposed that integrate the merits of both feature-based and intensity-based methods. Most focus on incorporating user provided or automatically extracted geometric feature constraints into the intensity-based energy functionals to achieve smoother and faster optimization. Typically they are more flexible, and designed in such way that either intensity information, such as gray values or gradients, is incorporated into a feature-based algorithm, or feature information, such as points or surfaces, is introduced to a pixel/voxel intensity-based algorithm. The hybrid methods are expected to be more efficient and robust than the pure-feature or pure-intensity based methods.
However, dealing with structures that appear or disappear between two images is still challenging. For instance, tumor growth/shrinkage in medical images acquired in the clinical tracking of treatment, trees/shadows or construction in aerial images taken at different times, and occlusion in other natural images often lead to significant differences in local image appearance. In addition, it is still difficult in general to match images acquired by sensors of different modalities, since different sensors, such as MRI, CT or PET, may produce very dissimilar images of the same scene. The relationship between the intensities of the matching pixels is often complex and not known a priori. Image noise and intensity inhomogeneity also add to this complexity. Furthermore, given two input images under arbitrary poses, recovering the globally optimal transformation efficiently is a hard problem due to the large parameter search space. To tackle these problems, the integration of both feature-based and intensity-based methods is very attractive since they are of complementary nature. While intensity-based methods are superior in multi-modal image matching and have better robustness to image noise and inhomogeneity, the feature-based methods are more natural to handle the structure appearing/disappearing problem, occlusion, and partial matching as well as to align images despite of their initial poses.